Machine learning-based application posture for zero trust networking

ABSTRACT

In one embodiment, a gateway to a zero trust network applies an access control policy to an endpoint device attempting to access a cloud-based application hosted by the zero trust network. The gateway acts as a reverse proxy between the endpoint device and the cloud-based application, based on the access control policy applied to the endpoint device. The gateway captures telemetry data regarding application traffic reverse proxied by the gateway between the endpoint device and the cloud-based application. The gateway detects an anomalous behavior of the application traffic by comparing the captured telemetry data to a machine learning-based behavioral model for the application. The gateway initiates a mitigation action for the detected anomalous behavior of the application traffic.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, moreparticularly, to machine learning based-application posture for zerotrust networking.

BACKGROUND

As applications continue to move to the cloud, security is increasinglybecoming a concern, with endpoint devices accessing a cloud-basedapplication from potentially anywhere in the world. Indeed, applicationshosted in the cloud are particularly susceptible to data exfiltrationwhereby a malicious entity gains access to otherwise protected data forpurposes of identity theft, fraud, or the like.

Zero trust networking is a relatively new paradigm that seeks to addresssome of the security concerns associated with cloud-based applications.In general, zero trust networking operates under the principle that allnetworks are untrusted, including both private enterprise networks andexternal public networks. Thus, even if a malicious entity were to gainaccess to an endpoint device in a private enterprise network, themalicious entity may be prevented from making lateral movements in thenetwork (e.g., from the infected device to a server), thanks to thesegmentation of network resources by application of zero trustnetworking principles to the network. However, while zero trustnetworking is effective in many scenarios, it also takes a relativelylimited approach to allowing access to a network resource. Notably, oncean endpoint device has been granted access to a resource, such as anapplication, the posture of the application is currently ignored underzero trust networking.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIGS. 1A-1B illustrate an example communication network;

FIG. 2 illustrates an example network device/node;

FIGS. 3A-3E illustrate an example zero trust network architecture; and

FIG. 4 illustrates an example simplified procedure for detectinganomalous behavior in a zero trust network.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a gateway to azero trust network applies an access control policy to an endpointdevice attempting to access a cloud-based application hosted by the zerotrust network. The gateway acts as a reverse proxy between the endpointdevice and the cloud-based application, based on the access controlpolicy applied to the endpoint device. The gateway captures telemetrydata regarding application traffic reverse proxied by the gatewaybetween the endpoint device and the cloud-based application. The gatewaydetects an anomalous behavior of the application traffic by comparingthe captured telemetry data to a machine learning-based behavioral modelfor the application. The gateway initiates a mitigation action for thedetected anomalous behavior of the application traffic.

DESCRIPTION

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,with the types ranging from local area networks (LANs) to wide areanetworks (WANs). LANs typically connect the nodes over dedicated privatecommunications links located in the same general physical location, suchas a building or campus. WANs, on the other hand, typically connectgeographically dispersed nodes over long-distance communications links,such as common carrier telephone lines, optical lightpaths, synchronousoptical networks (SONET), or synchronous digital hierarchy (SDH) links,or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, andothers. The Internet is an example of a WAN that connects disparatenetworks throughout the world, providing global communication betweennodes on various networks. The nodes typically communicate over thenetwork by exchanging discrete frames or packets of data according topredefined protocols, such as the Transmission Control Protocol/InternetProtocol (TCP/IP). In this context, a protocol consists of a set ofrules defining how the nodes interact with each other. Computer networksmay be further interconnected by an intermediate network node, such as arouter, to extend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are aspecific type of network having spatially distributed autonomous devicessuch as sensors, actuators, etc., that cooperatively monitor physical orenvironmental conditions at different locations, such as, e.g.,energy/power consumption, resource consumption (e.g., water/gas/etc. foradvanced metering infrastructure or “AMI” applications) temperature,pressure, vibration, sound, radiation, motion, pollutants, etc. Othertypes of smart objects include actuators, e.g., responsible for turningon/off an engine or perform any other actions. Sensor networks, a typeof smart object network, are typically shared-media networks, such aswireless or PLC networks. That is, in addition to one or more sensors,each sensor device (node) in a sensor network may generally be equippedwith a radio transceiver or other communication port such as PLC, amicrocontroller, and an energy source, such as a battery. Often, smartobject networks are considered field area networks (FANs), neighborhoodarea networks (NANs), personal area networks (PANs), etc. Generally,size and cost constraints on smart object nodes (e.g., sensors) resultin corresponding constraints on resources such as energy, memory,computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100illustratively comprising nodes/devices, such as a plurality ofrouters/devices interconnected by links or networks, as shown. Forexample, customer edge (CE) routers 110 may be interconnected withprovider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order tocommunicate across a core network, such as an illustrative networkbackbone 130. For example, routers 110, 120 may be interconnected by thepublic Internet, a multiprotocol label switching (MPLS) virtual privatenetwork (VPN), or the like. Data packets 140 (e.g., traffic/messages)may be exchanged among the nodes/devices of the computer network 100over links using predefined network communication protocols such as theTransmission Control Protocol/Internet Protocol (TCP/IP), User DatagramProtocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relayprotocol, or any other suitable protocol. Those skilled in the art willunderstand that any number of nodes, devices, links, etc. may be used inthe computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connectedto a private network (e.g., dedicated leased lines, an optical network,etc.) or a virtual private network (VPN), such as an MPLS VPN thanks toa carrier network, via one or more links exhibiting very differentnetwork and service level agreement characteristics. For the sake ofillustration, a given customer site may fall under any of the followingcategories:

1.) Site Type A: a site connected to the network (e.g., via a private orVPN link) using a single CE router and a single link, with potentially abackup link (e.g., a 3G/4G/LTE backup connection). For example, aparticular CE router 110 shown in network 100 may support a givencustomer site, potentially also with a backup link, such as a wirelessconnection. 2.) Site Type B: a site connected to the network using twoMPLS VPN links (e.g., from different Service Providers), withpotentially a backup link (e.g., a 3G/4G/LTE connection). A site of typeB may itself be of different types:

2a.) Site Type B1: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/LTE connection).

2b.) Site Type B2: a site connected to the network using one MPLS VPNlink and one link connected to the public Internet, with potentially abackup link (e.g., a 3G/4G/LTE connection). For example, a particularcustomer site may be connected to network 100 via PE-3 and via aseparate Internet connection, potentially also with a wireless backuplink.

2c.) Site Type B3: a site connected to the network using two linksconnected to the public Internet, with potentially a backup link (e.g.,a 3G/4G/LTE connection).

Notably, MPLS VPN links are usually tied to a committed service levelagreement, whereas Internet links may either have no service levelagreement at all or a loose service level agreement (e.g., a “GoldPackage” Internet service connection that guarantees a certain level ofperformance to a customer site).

3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but withmore than one CE router (e.g., a first CE router connected to one linkwhile a second CE router is connected to the other link), andpotentially a backup link (e.g., a wireless 3G/4G/LTE backup link). Forexample, a particular customer site may include a first CE router 110connected to PE-2 and a second CE router 110 connected to PE-3.

FIG. 1B illustrates an example of network 100 in greater detail,according to various embodiments. As shown, network backbone 130 mayprovide connectivity between devices located in different geographicalareas and/or different types of local networks. For example, network 100may comprise local/branch networks 160, 162 that include devices/nodes10-16 and devices/nodes 18-20, respectively, as well as a datacenter/cloud environment 150 that includes servers 152-154. Notably,local networks 160-162 and data center/cloud environment 150 may belocated in different geographic locations.

Servers 152-154 may include, in various embodiments, a networkmanagement server (NMS), a dynamic host configuration protocol (DHCP)server, a constrained application protocol (CoAP) server, an outagemanagement system (OMS), an application policy infrastructure controller(APIC), an application server, etc. As would be appreciated, network 100may include any number of local networks, data centers, cloudenvironments, devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to othernetwork topologies and configurations. For example, the techniquesherein may be applied to peering points with high-speed links, datacenters, etc.

In various embodiments, network 100 may include one or more meshnetworks, such as an Internet of Things network. Loosely, the term“Internet of Things” or “IoT” refers to uniquely identifiable objects(things) and their virtual representations in a network-basedarchitecture. In particular, the next frontier in the evolution of theInternet is the ability to connect more than just computers andcommunications devices, but rather the ability to connect “objects” ingeneral, such as lights, appliances, vehicles, heating, ventilating, andair-conditioning (HVAC), windows and window shades and blinds, doors,locks, etc. The “Internet of Things” thus generally refers to theinterconnection of objects (e.g., smart objects), such as sensors andactuators, over a computer network (e.g., via IP), which may be thepublic Internet or a private network.

Notably, shared-media mesh networks, such as wireless or PLC networks,etc., are often on what is referred to as Low-Power and Lossy Networks(LLNs), which are a class of network in which both the routers and theirinterconnect are constrained: LLN routers typically operate withconstraints, e.g., processing power, memory, and/or energy (battery),and their interconnects are characterized by, illustratively, high lossrates, low data rates, and/or instability. LLNs are comprised ofanything from a few dozen to thousands or even millions of LLN routers,and support point-to-point traffic (between devices inside the LLN),point-to-multipoint traffic (from a central control point such at theroot node to a subset of devices inside the LLN), andmultipoint-to-point traffic (from devices inside the LLN towards acentral control point). Often, an IoT network is implemented with anLLN-like architecture. For example, as shown, local network 160 may bean LLN in which CE-2 operates as a root node for nodes/devices 10-16 inthe local mesh, in some embodiments.

In contrast to traditional networks, LLNs face a number of communicationchallenges. First, LLNs communicate over a physical medium that isstrongly affected by environmental conditions that change over time.Some examples include temporal changes in interference (e.g., otherwireless networks or electrical appliances), physical obstructions(e.g., doors opening/closing, seasonal changes such as the foliagedensity of trees, etc.), and propagation characteristics of the physicalmedia (e.g., temperature or humidity changes, etc.). The time scales ofsuch temporal changes can range between milliseconds (e.g.,transmissions from other transceivers) to months (e.g., seasonal changesof an outdoor environment). In addition, LLN devices typically uselow-cost and low-power designs that limit the capabilities of theirtransceivers. In particular, LLN transceivers typically provide lowthroughput. Furthermore, LLN transceivers typically support limited linkmargin, making the effects of interference and environmental changesvisible to link and network protocols. The high number of nodes in LLNsin comparison to traditional networks also makes routing, quality ofservice (QoS), security, network management, and traffic engineeringextremely challenging, to mention a few.

FIG. 2 is a schematic block diagram of an example node/device 200 thatmay be used with one or more embodiments described herein, e.g., as anyof the computing devices shown in FIGS. 1A-1B, particularly the PErouters 120, CE routers 110, nodes/device 10-20, servers 152-154 (e.g.,a network controller located in a data center, etc.), any othercomputing device that supports the operations of network 100 (e.g.,switches, etc.), or any of the other devices referenced below. Thedevice 200 may also be any other suitable type of device depending uponthe type of network architecture in place, such as IoT nodes, etc.Device 200 comprises one or more network interfaces 210, one or moreprocessors 220, and a memory 240 interconnected by a system bus 250, andis powered by a power supply 260.

The network interfaces 210 include the mechanical, electrical, andsignaling circuitry for communicating data over physical links coupledto the network 100. The network interfaces may be configured to transmitand/or receive data using a variety of different communicationprotocols. Notably, a physical network interface 210 may also be used toimplement one or more virtual network interfaces, such as for virtualprivate network (VPN) access, known to those skilled in the art.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor(s) 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise necessaryelements or logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242 (e.g., theInternetworking Operating System, or IOS®, of Cisco Systems, Inc.,another operating system, etc.), portions of which are typicallyresident in memory 240 and executed by the processor(s), functionallyorganizes the node by, inter alia, invoking network operations insupport of software processors and/or services executing on the device.These software processors and/or services may comprise a networksecurity process 248, as described herein, any of which mayalternatively be located within individual network interfaces.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while processes may be shown and/or describedseparately, those skilled in the art will appreciate that processes maybe routines or modules within other processes.

In general, network security process 248 may execute one or more machinelearning-based classifiers to assess traffic in the network anddetermine the posture of the source application of the traffic. In turn,network security process 248 may apply any number of network securitypolicies to the traffic, based on the posture of the underlyingapplication.

Network security process 248 may employ any number of machine learningtechniques, to classify gathered traffic telemetry data. In general,machine learning is concerned with the design and the development oftechniques that receive empirical data as input (e.g., telemetry dataregarding traffic in the network) and recognize complex patterns in theinput data. For example, some machine learning techniques use anunderlying model M, whose parameters are optimized for minimizing thecost function associated to M, given the input data. For instance, inthe context of classification, the model M may be a straight line thatseparates the data into two classes (e.g., labels) such that M=a*x+b*y+cand the cost function is a function of the number of misclassifiedpoints. The learning process then operates by adjusting the parametersa,b,c such that the number of misclassified points is minimal. Afterthis optimization/learning phase, security process 248 can use the modelM to classify new data points, such as information regarding new trafficflows in the network. Often, M is a statistical model, and the costfunction is inversely proportional to the likelihood of M, given theinput data.

In various embodiments, network security process 248 may employ one ormore supervised, unsupervised, or semi-supervised machine learningmodels. Generally, supervised learning entails the use of a training setof data, as noted above, that is used to train the model to apply labelsto the input data. For example, the training data may include sampletelemetry data that is “normal application behavior,” or “anomalousapplication behavior.” On the other end of the spectrum are unsupervisedtechniques that do not require a training set of labels. Notably, whilea supervised learning model may look for previously seen attack patternsthat have been labeled as such, an unsupervised model may instead lookto whether there are sudden changes in the behavior of the networktraffic and/or underlying application. Semi-supervised learning modelstake a middle ground approach that uses a greatly reduced set of labeledtraining data.

Example machine learning techniques that network security process 248can employ may include, but are not limited to, nearest neighbor (NN)techniques (e.g., k-NN models, replicator NN models, etc.), statisticaltechniques (e.g., Bayesian networks, etc.), clustering techniques (e.g.,k-means, mean-shift, etc.), neural networks (e.g., reservoir networks,artificial neural networks, etc.), support vector machines (SVMs),logistic or other regression, Markov models or chains, principalcomponent analysis (PCA) (e.g., for linear models), multi-layerperceptron (MLP) ANNs (e.g., for non-linear models), replicatingreservoir networks (e.g., for non-linear models, typically for timeseries), random forest classification, or the like.

The performance of a machine learning model can be evaluated in a numberof ways based on the number of true positives, false positives, truenegatives, and/or false negatives of the model. For example, the falsepositives of the model may refer to the number of traffic flows that areincorrectly classified as indicative of anomalous application behavior.Conversely, the false negatives of the model may refer to the number oftraffic flows that the model incorrectly classifies as being indicativeof normal application behavior. True negatives and positives may referto the number of traffic flows that the model correctly classifies asindicative of normal or abnormal application behavior, respectively.Related to these measurements are the concepts of recall and precision.Generally, recall refers to the ratio of true positives to the sum oftrue positives and false negatives, which quantifies the sensitivity ofthe model. Similarly, precision refers to the ratio of true positivesthe sum of true and false positives.

In some cases, network security process 248 may assess the capturedtelemetry data on a per-flow basis. In other embodiments, networksecurity process 248 may assess telemetry data for a plurality oftraffic flows based on any number of different conditions. For example,traffic flows may be grouped for analysis based on their sources,destinations, temporal characteristics (e.g., flows that occur aroundthe same time, etc.), combinations thereof, or based on any other set offlow characteristics.

As noted above, zero trust networking operates under the principle thatall networks are untrusted. Typical zero trust networking solutions,such as BeyondCorp and DUO Beyond, base access policy decisions on theconcepts of trusted users and trusted devices. Under such models, if theuser provides credentials for a trusted user profile, and the user isalso operating an endpoint device that is trusted (e.g., based on make,model, location, etc.), the user will be allowed access to the requestedapplication. However, this approach has the following drawbacks:

-   -   User and device trust are verified only when the end user first        tries to connect to an application server. After a session is        established, there is no further check done by the zero trust        networking architecture.    -   Current zero trust networking architectures also do not take        into account the posture of the application being used. In other        words, once the user is allowed access to the application, the        interactions between the end user and the application are        completely ignored under current zero trust networking        architectures.

Machine Learning-Based Application Posture for Zero Trust Networking

The techniques herein introduce a machine learning-based mechanism toincorporate application posture verification into zero trust networkingarchitectures. In some aspects, supervised learning can be leveraged toperform application posture analysis during the entire lifecycle of anapplication session. Incorporating the application posture into a zerotrust networking architecture can significantly improve the overallsecurity of the network, as well as provide granular access controlmediation actions, in the event that a behavioral anomaly is detected.

Specifically, according to one or more embodiments of the disclosure asdescribed in detail below, a gateway to a zero trust network applies anaccess control policy to an endpoint device attempting to access acloud-based application hosted by the zero trust network. The gatewayacts as a reverse proxy between the endpoint device and the cloud-basedapplication, based on the access control policy applied to the endpointdevice. The gateway captures telemetry data regarding applicationtraffic reverse proxied by the gateway between the endpoint device andthe cloud-based application. The gateway detects an anomalous behaviorof the application traffic by comparing the captured telemetry data to amachine learning-based behavioral model for the application. The gatewayinitiates a mitigation action for the detected anomalous behavior of theapplication traffic.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with thenetwork security process 248, which may include computer executableinstructions executed by the processor 220 (or independent processor ofinterfaces 210) to perform functions relating to the techniquesdescribed herein.

Operationally, FIGS. 3A-3E illustrate an example zero trust networkarchitecture 300, according to various embodiments. As shown in FIG. 3A,at the core of architecture 300 is a zero trust network (ZTN) gateway302 (e.g., a device 200) that executes network security process 248, toprovide controlled access to the resources in zero trust network 320. Ingeneral, network security process 248 may comprise any or all of thefollowing components: an admission control module 304, a reverse proxymodule 306, an application traffic analyzer 308, and/or an applicationposture analyzer 310. As would be appreciated, these components 304-310may be implemented in a distributed manner or implemented as their ownstand-alone services, either as a local or remote service that operatesin conjunction with ZTN access gateway 302. In addition, thefunctionalities of components 304-310 may be combined, omitted, orimplemented as part of other processes, as desired.

Hosted within zero trust network 320 may be any number of cloud-basedapplications, such as software as a service (SaaS) cloud application(s)314, enterprise application(s) 316 in an infrastructure as a service(IaaS) cloud, and/or enterprise application(s) 318 hosted in adatacenter. All access requests for applications 314-318 may be steeredthrough ZTN access gateway 302, such as by pointing the DNS CNAMErecords for applications 314-318 towards the IP address of ZTN accessgateway 302. For example, assume that an endpoint device 312 locatedwithin any network, be it a public or private network, requests accessto one of SaaS cloud applications 314 via an access request 322. In sucha case, the request may be directed towards ZTN access gateway 302.

As shown in FIG. 3B, after ZTN access gateway 302 receives accessrequest 322 from endpoint device 312, admission control module 304 mayfirst determine whether to allow endpoint device 312 to access therequested application 314. To do so, admission control module 304 mayperform any or all of the following:

-   -   Verify User Trust—In general, this step entails admission        control module 304 determining whether the user of endpoint        device 312 is authorized to access the requested application        314. For example, access request 304 may include information        regarding the user profile of the user of endpoint device 312.        In another example, assume that endpoint device 312 participates        in a single sign on (SSO) mechanism with an enterprise or        cloud-based Identity Management System (IDM). In such a case,        admission control module 304 may operate in conjunction with the        IDM, to authenticate the user and the level of trust associated        with the user's profile.    -   Verify Device Trust—In addition to verify the user trust,        admission control module 304 may also determine the trust of        endpoint device 312 attempting to access one of applications        314. For example, admission control module 304 may compare        device information regarding endpoint device 312 (e.g., as        included in request 322) to an inventory of devices and/or        device postures, to determine whether endpoint device 312 is        trusted. For example, admission control module 304 may evaluate        the location of endpoint device 312, the make/model/software        configuration of endpoint device 312, and/or other device        information, to determine whether endpoint device 312 is trusted        to access the requested application 314.    -   Apply Access Control Policy/Policies: Once admission control        module 304 has verified the trust of endpoint device 312 and the        user profile of the user of endpoint device 312, admission        control module 304 may apply any number of group-based access        control policies to the application session. Such access control        polices could vary based up on the location of endpoint device        312, device trust level, etc. Of course, if endpoint device 312        and/or the user of endpoint device 312 is untrusted, admission        control module 304 may block endpoint device 312 from accessing        the requested application 314.

As shown in FIG. 3C, if admission control module 304 determines thatendpoint device 312 is trusted to access the application requested byaccess request 322, reverse proxy module 306 may operate as a reverseproxy on behalf of the requested application 314. In general, reverseproxying entails an intermediary, such as ZTN access gateway 302,accessing a resource (e.g., the requested application 314) on behalf ofendpoint device 312 and returning data to endpoint device 312 on behalfof application 314. In other words, reverse proxy module 306 may act asa “reverse” proxy in that endpoint device 312 may interact with ZTNaccess gateway 302 under the belief that it is actually interactingdirectly with application 314. While acting as a reverse proxy betweencloud application 314 and endpoint device 312, reverse proxy module 306may then relay application traffic 324 between the two.

While the admission control functions of admission control module 304,coupled with the reverse proxy functions of reverse proxy module 306,are often enough to prevent most security breaches from occurring, thereis still the possibility under current ZTN paradigms for dataexfiltration to occur after granting an endpoint access to anapplication. Accordingly, the techniques herein also introduce a closedloop monitoring mechanism for the application session, as well as amechanism that assesses the posture of the application throughout thelifecycle of the application session.

As shown in FIG. 3D, while an application session is active betweenendpoint device 312 and a cloud-based application, such as cloudapplication 314, application traffic analyzer 308 may capture telemetryregarding application traffic 324. In other words, while applicationtraffic 324 is send through ZTN access gateway 302 during its operationsas a reverse proxy on behalf of cloud application 314, applicationtraffic analyzer 308 may also capture telemetry data regardingapplication traffic 324.

Example telemetry information that application traffic analyzer 308 maycapture regarding application traffic 324 may include, but is notlimited to, any or all of the following:

-   -   Packet size information—e.g., maximum, minimum, average packet        size, etc.    -   Packet count information—e.g., maximum, minimum, average packet        count, etc.    -   Packet timing information—e.g., maximum, minimum, average flow        duration, etc.    -   Packet header information    -   Packet payload information—e.g., which functions endpoint device        312 seeks to perform within application 314 and/or the data that        endpoint device 312 seeks to access within application 314.

As shown in FIG. 3E, application posture analyzer 310 may then assessthe telemetry data captured by application traffic analyzer 308, todetermine whether the behavior of application traffic 324 is anomalous,according to various embodiments. In other words, application postureanalyzer 310 may continuously assess the posture of the applicationduring an application session with one of applications 314-318. Ifapplication posture analyzer 310 detects such a behavioral anomaly, itmay then initiate a mitigation action, such as ending the applicationsession/blocking application traffic 324, sending an alert to a userinterface operated by a security expert, limiting access privilegesduring the session or demoting endpoint device 312 to a more restrictiveaccess policy, combinations thereof, or the like.

Note that for the same application, the expected application posture canvary on a per user group basis, such as according to a group-basedpolicy applied by admission control module 304. Thus, in someembodiments, application posture analyzer 310 may also take as input theadmission/access policy applied to endpoint device 312 by admissioncontrol module 304 and/or the factors used by module 304 to assign sucha policy to device 312 (e.g., user profile, device information, etc.).Indeed, even for the same user, the expected application posture can bedifferent depending up on the user location, device trust level, etc.

In some embodiments, application posture analyzer 310 may include amachine learning-based behavioral model, to detect behavioral anomalies.For example, such a model may model ‘normal’ vs. ‘abnormal’ applicationbehaviors and application posture analyzer 310 can compare its inputdate (e.g., the captured telemetry data regarding application traffic324, policy information from module 304, etc.) to the model, todetermine whether the behavior of the session is normal or abnormal. Inone embodiment, such a behavioral model may take the form of one or moresupervised learning-based classifiers that apply a label to the inputdata to application posture analyzer 310 (e.g., ‘normal’ or ‘abnormal’).In another embodiment, such a behavioral model may be learned over timeusing unsupervised learning.

Training of the one or more machine learning classifiers of applicationposture analyzer 310 can be achieved either in an online or offlinemanner. For example, offline training can be achieved by obtaininglabeled training data from the various stakeholders involved, such asthe following:

-   -   Cloud-based application providers: Application providers who are        part of the solution ecosystem will be in a position to provide        lab generated reference data which closely resembles the        production traces. In addition, application providers could        provide sanitized versions of production application data which        will remove personally identifiable information (e.g., the        identity of an end user, etc.). In the latter case, the        confidentiality will not be an issue since applications        typically use encryption technologies, such as transport layer        security (TLS).    -   Enterprise IT & InfoSec: If the posture analysis needs to be        customized to the expected application behavior per user group        of an enterprise, the training data may be provided or labeled        by that enterprise.

As indicated earlier, supervised machine learning models can be used forthe application posture analysis of application posture analyzer 310.Typically, the objective of such a model is NOT to classify a singleapplication traffic flow, but instead to detect behavioral anomalies inthe application stream between the endpoint device 312 and theapplication (e.g., application 314), based on the expected applicationposture. For example, a random forest-based classifier may be trainedusing labeled data to identify outlier behaviors in the interactionsbetween the endpoint device and the application.

For illustrative purposes, the following examples are provided ofpotential behavioral anomalies that application posture analyzer 310 maydetect:

-   -   A specific application is expected to have a maximum burst of N        kbps per flow and average rate of N. Anything that exceeds these        limits could indicate an anomaly.    -   A user belongs to a group-based policy for an application which        only has read only access to the files and no permissions        available to edit. If the user edits the files, that can        indicate compromised access and, thus, a behavioral anomaly.    -   For the same enterprise application, a user may have different        access privileges depending up on the following: a.) whether the        endpoint device is trusted or untrusted and b.) whether the user        is located on an enterprise premise or is roaming.

FIG. 4 illustrates an example simplified procedure for detectinganomalous behavior in a zero trust network, in accordance with one ormore embodiments described herein. For example, a non-generic,specifically configured device (e.g., device 200), such as a ZTN accessgateway, may perform procedure 400 by executing stored instructions(e.g., process 248). The procedure 400 may start at step 405, andcontinues to step 410, where, as described in greater detail above, thegateway applies an access control policy to an endpoint deviceattempting to access a cloud-based application hosted by the zero trustnetwork. Such a policy may be based on any or all of the following: auser profile of the user operating the endpoint device, theprofile/posture of the endpoint device itself (e.g., location, make,model, type, etc.), and/or the application for which the endpoint deviceseeks access.

At step 415, as detailed above, the gateway may act as a reverse proxybetween the endpoint device and the cloud-based application, based onthe access control policy applied to the endpoint device. For example,if the endpoint device is authorized to access the application, it mayinteract with the gateway instead of the application itself, with thegateway serving as a proxy on behalf of the application.

At step 420, the gateway may capture telemetry data regardingapplication traffic reverse proxied by the gateway between the endpointdevice and the cloud-based application, as described in greater detailabove. Notably, as the gateway is acting as a reverse proxy on behalf ofthe application, the gateway is also uniquely positioned to capturetelemetry data regarding the interactions between the endpoint deviceand the application. In various embodiments, the telemetry data may beindicative of the packet size of the traffic, the packet timing of thetraffic, flow data rate, or even the transactions that the endpointdevice seeks to perform within the application (e.g., reading certaindata, writing certain data, or editing certain data).

At step 425, as detailed above, the gateway may detect an anomalousbehavior of the application traffic by comparing the captured telemetrydata to a machine learning-based behavioral model for the application.In general, such a model may model the ‘normal’ posture of theapplication. In some embodiments, the model may take the form of asupervised learning classifier that is trained to distinguish between‘normal’ and ‘anomalous’ behaviors. In further embodiments, the modelmay also take as input the user profile information (e.g., the group towhich the user belongs), so as to also model different behaviors fordifferent groups of users of the application.

At step 430, the gateway may initiate a mitigation action for thedetected anomalous behavior of the application traffic, as described ingreater detail above. For example, the mitigation action may entailblocking the application traffic/ending the application session, sendingan alert to a user interface regarding the detected anomalous behavior,or lowering the permission level of the endpoint device within theapplication. Procedure 400 then ends at step 435.

It should be noted that while certain steps within procedure 400 may beoptional as described above, the steps shown in FIG. 4 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, introduce a closed loopmechanism for use in zero trust networks that also takes into accountthe application posture of the application. Indeed, rather than simplyallowing or blocking access to the application, the techniques hereinalso provide for the continual monitoring of how the endpointdevice/user interacts with the application. If the behavior becomesanomalous during the application session, mitigation actions can then beinitiated.

While there have been shown and described illustrative embodiments thatprovide for using machine learning to assess application posture in azero trust network, it is to be understood that various otheradaptations and modifications may be made within the spirit and scope ofthe embodiments herein. For example, while certain embodiments aredescribed herein with respect to using certain models for purposes ofanomaly detection, the models are not limited as such and may be usedfor other functions, in other embodiments. In addition, while certainprotocols are shown, other suitable protocols may be used, accordingly.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly, this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true spirit and scope of theembodiments herein.

What is claimed is:
 1. A method comprising: applying, by a gateway to azero trust network, an access control policy to an endpoint deviceattempting to access a cloud-based application hosted by the zero trustnetwork; in response to the access control policy indicating that theendpoint device is allowed to access the cloud-based application:acting, by the gateway, as a reverse proxy between the endpoint deviceand the cloud-based application, capturing, by the gateway, telemetrydata regarding application traffic reverse proxied by the gatewaybetween the endpoint device and the cloud-based application, detecting,by the gateway, an anomalous behavior of the application traffic byusing the captured telemetry data and user profile informationassociated with the endpoint device as input to a supervisedlearning-based classifier that is trained to distinguish between normaland anomalous behaviors of the application, and initiating, by thegateway, a mitigation action for the detected anomalous behavior of theapplication traffic; and in response to the access control policyindicating that the endpoint device is not allowed to access thecloud-based application, blocking, by the gateway, the endpoint devicefrom accessing the cloud-based application.
 2. The method as in claim 1,wherein the mitigation action comprises blocking the application trafficor sending an alert to a user interface regarding the detected anomalousbehavior.
 3. The method as in claim 1, wherein the gateway applies theaccess control policy to the endpoint device based on a user profileassociated with the endpoint device, a device profile of the endpointdevice, and the cloud-based application.
 4. The method as in claim 1,wherein the telemetry data is indicative of a flow data rate of theapplication traffic.
 5. The method as in claim 1, further comprising:obtaining a training dataset that includes application traffic betweenthe cloud-based application and a plurality of endpoint devices; andusing the training dataset to train the machine learning-basedbehavioral model for the application.
 6. An apparatus, comprising: oneor more network interfaces to communicate with a zero trust network; aprocessor coupled to the network interfaces and configured to executeone or more processes; and a memory configured to store a processexecutable by the processor, the process when executed configured to:apply an access control policy to an endpoint device attempting toaccess a cloud-based application hosted by the zero trust network; inresponse to the access control policy indicating that the endpointdevice is allowed to access the cloud-based application: act as areverse proxy between the endpoint device and the cloud-basedapplication, capture telemetry data regarding application trafficreverse proxied by the gateway between the endpoint device and thecloud-based application, detect an anomalous behavior of the applicationtraffic by using the captured telemetry data and user profileinformation associated with the endpoint device as input to a supervisedlearning-based classifier that is trained to distinguish between normaland anomalous behaviors of the application, and initiate a mitigationaction for the detected anomalous behavior of the application traffic;and in response to the access control policy indicating that theendpoint device is not allowed to access the cloud-based application,block the endpoint device from accessing the cloud-based application. 7.The apparatus as in claim 6, wherein the mitigation action comprisesblocking the application traffic or sending an alert to a user interfaceregarding the detected anomalous behavior.
 8. The apparatus as in claim6, wherein the gateway applies the access control policy to the endpointdevice based on a user profile associated with the endpoint device, adevice profile of the endpoint device, and the cloud-based application.9. The apparatus as in claim 6, wherein the telemetry data is indicativeof a flow data rate of the application traffic.
 10. The apparatus as inclaim 6, wherein the process when executed is further configured to:obtain a training dataset that includes application traffic between thecloud-based application and a plurality of endpoint devices; and use thetraining dataset to train the machine learning-based behavioral modelfor the application.
 11. A tangible, non-transitory, computer-readablemedium storing program instructions that cause a gateway to a zero trustnetwork to execute a process comprising: applying, by the gateway to thezero trust network, an access control policy to an endpoint deviceattempting to access a cloud-based application hosted by the zero trustnetwork; in response to the access control policy indicating that theendpoint device is allowed to access the cloud-based application:acting, by the gateway, as a reverse proxy between the endpoint deviceand the cloud-based application, capturing, by the gateway, telemetrydata regarding application traffic reverse proxied by the gatewaybetween the endpoint device and the cloud-based application, detecting,by the gateway, an anomalous behavior of the application traffic byusing the captured telemetry data and user profile informationassociated with the endpoint device as input to a supervisedlearning-based classifier that is trained to distinguish between normaland anomalous behaviors of the application, and initiating, by thegateway, a mitigation action for the detected anomalous behavior of theapplication traffic; and in response to the access control policyindicating that the endpoint device is not allowed to access thecloud-based application, blocking, by the gateway, the endpoint devicefrom accessing the cloud-based application.
 12. The computer-readablemedium as in claim 11, wherein the mitigation action comprises blockingthe application traffic or sending an alert to a user interfaceregarding the detected anomalous behavior.
 13. The computer-readablemedium as in claim 11, wherein the gateway applies the access controlpolicy to the endpoint device based on a user profile associated withthe endpoint device, a device profile of the endpoint device, and thecloud-based application.
 14. The computer-readable medium as in claim11, wherein the telemetry data is indicative of a flow data rate of theapplication traffic.
 15. The computer-readable medium as in claim 11,wherein the process further comprises: obtaining a training dataset thatincludes application traffic between the cloud-based application and aplurality of endpoint devices; and using the training dataset to trainthe machine learning-based behavioral model for the application.